Forthcoming articles

 


International Journal of Business Intelligence and Systems Engineering

 

These articles have been peer-reviewed and accepted for publication in IJBISE, but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

 

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International Journal of Business Intelligence and Systems Engineering (4 papers in press)

 

Regular Issues

 

  • BENCHMARKING THE OPERATING EFFICIENCY OF U.S. REGIONAL BANKS   Order a copy of this article
    by D.K. Malhotra, Rashmi Malhotra, Ruben Mendoza 
    Abstract: Banks, as private entities with a public purpose, strike a balance by achieving high profitability levels while managing the risks involved. Thus, understanding the factors that drive an efficiently-operating bank is an important issue. This study illustrates the use of data envelopment analysis (DEA) methodology to benchmark the operating efficiency of thirty four U.S. Regional banks during the period 2009 to 2013. The study finds that only three banks out of thirty-four banks were 100% efficient relative to others throughout the sample period of 2009 to 2013. In addition, we also use the DEAs slack analysis to understand the factors responsible for the poor performance of a bank. Finally, we also investigate the factors contributing to the performance of the banking industry by covering a time period spanning the start of the economic crisis and the consequent passing of new laws to regulate the financial services industry.
    Keywords: Regional banks, Data Envelopment Analysis, Benchmarking, Operating Efficiency

  • Operational Intelligence through Performance Trends: An Oracle Prototype   Order a copy of this article
    by Rajeev Kaula 
    Abstract: Improving business process performance through operational intelligence is essential to enhance an organizations ability to achieve business objectives and competitive advantage. This paper proposes an extension on insights provided by traditional business intelligence analytics through star schema that goes beyond snapshots of business process performance to outlining details on factors influencing business process operations over a period of time. Such extended insights referred as performance trends enable a richer assessment of business process performance. Performance trends essentially allows a business to determine whether the direction of business process performance is going up, down, or staying flat with respect to some success measure over a period of time. It is expressed as an analytic business rule that can be applied on the business process through analytic triggers. The paper illustrates the concepts through a prototype that is adapted from the Oracles E-Business Suite Lead to Forecast business process. The prototype is implemented in Oracles PL/SQL language.
    Keywords: Business Intelligence; Operational Intelligence; Business Process; Business Rules; Oracle; PL/SQL.

  • A Comparative Analysis of Classifiers in Cancer Prediction Using Multiple Data Mining Techniques   Order a copy of this article
    by Seyed Mohammad Jafar Jalali, Sergio Moro, Mohammad Reza Mahmoudi, Keramat Allah Ghaffary, Mohsen Maleki, Aref Alidoostan 
    Abstract: In recent years, application of data mining methods in health industry has received increased attention from both health professionals and scholars. This paper presents a data mining framework for detecting breast cancer based on real data from one of Iran hospitals by applying association rules and the most commonly used classifiers. The former were adopted for reducing the size of datasets, while the latter were chosen for cancer prediction. A k-fold cross validation procedure was included for evaluating the performance of the proposed classifiers. Among the six classifiers used in this paper, support vector machine achieved the best results, with an accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well.
    Keywords: Cancer Prediction; Data Mining; Classifiers; Association Rules.

  • EVALUATING BANK SOLVENCY WITH SUPPORT VECTOR MACHINES   Order a copy of this article
    by D.K. Malhotra, Robert Nydick, Kunal Malhotra 
    Abstract: Banks as financial intermediaries play a very useful role in economic growth by facilitating the flow of funds to various sectors of the economy. Deterioration in a banks performance and potential failure of the bank may lead to loss of confidence in the financial system that can result loss of household savings and non-availability of funds to the business sector for economic expansion and growth. Banking regulators around are always looking for ways to identifying sooner the banks that can be at risk of failure so that corrective action can be taken with minimal disruption to the economy. This study illustrates the use of support vector machines, an artificial intelligence technique, to predict the pending insolvency of a bank so that regulators can take appropriate steps to prevent a domino effect. To alleviate the problem of bias in the training set and to examine the robustness of support vector machine classifiers in identifying unsafe banks, we cross-validate our results through different samples of the data.
    Keywords: bank failure; artificial intelligence; support vector machines.